Call nowFree Demo


Follow Us

For Special Diwali Offers Call Us @ +91-9999491958

Industrial Training

9999491895 | 9873140018

Tableau Practical Training

  • Overview Of Tableau
  • Tableau Architecture
  • Installation And Configuration Of Tableau 10
  • Managing Metadata
  • Managing Extracts
  • Data Sources
  • Cross-Database Joins
  • Data Aggregation And Data Ports
  • Tableau Charts
  • Bar Charts and Stacked Bars Data Blending
  • Tree Maps and Scatter Plots
  • Individual Axes, Blended Axes, Dual Axes and Combinational Chart
  • Drill Down and Hierarchies
  • Sorting, Filtering and Grouping
  • Parameters and Formatting
  • Trend and Reference Lines
  • Forecasting and Clustering
  • Analysis with Cubes and MDX
  • Connecting Tableau to Data File
  • Navigating Tableau
  • Calculated Fields
  • Adding Colors, Labels and Formatting
  • Data Extracts and Time Series
  • Understanding Granularity, Aggregation and Level Of Details
  • Default Location in Maps
  • Custom Geo Coding
  • Symbol Map and Filled Map
  • Into Section
  • Joining Data In Tableau
  • Working With Maps and Hierarchies
  • Scatter Plot and Applying Filters in Different Sheets
  • Creating First Dashboard
  • Duplicate Values
  • Multiple Fields
  • Data Blending
  • Dual Axis Chart
  • Building Calculated Fields
  • Downloading Data set and Connection
  • Mapping
  • Building Table Calculation for Gender
  • Bins and Distributions for Age
  • Tree Map Chart
  • Advanced Dashboard
  • Storyline and Storytelling
  • Data Format
  • Data Interpreter
  • Multiple Columns And Pivot
  • Metadata Grid
  • Advanced Data Preparation

Inquiry for Tableau Training

Please find the Tableau Training Course Duration.
Course Module Course Duration
Tableau 30 – 40 Hours




What is Data Visualization?

Data visualization is the representation of the data in a graphical and pictorial form which allows the user to understand the information quickly and help in decision making. It is used widely by organizations for business intelligence and help them predict the market condition. It enables the data manager and analyst to visualize and examine the information correctly and grasp the complex concepts to identify the patterns and take necessary actions for future growth. Seeing the information in charts and tables tends to be understood easier than reports and spreadsheets. It helps in identifying areas that need improvement, clarify the factors that influence the customers and predict future and sales.

Differentiate between parameters and filters in Tableau.

The difference between parameters and filters in Tableau can be listed below:

  • Parameters helps the user to insert values in the form of float, integers, string, date, etc., which can be used in calculations. Whereas filters is used only filter the values of the list as per the user’s command, which cannot be calculated.
  • Users is able to dynamically change the measures and dimensions in parameter, which is not the case with filters.
  • Parameters are used for constant values in the field while filters makes a subset.
  • There are four types of filters: Context filter, Quick Filter, Cascade filter, Action filter.
What are Quick Filters in Tableau?

Quick filter is a type of filter used in Tableau which is used to quickly filter the results from the list. The user can simply right click from the mouse in Tableau to get the quick filter dialogue box where one can easily filter their desired results. There are a total of 8 quick filters which are:

Single Value        Select one value at a time in a list.

Single Value        Select a single value in a drop-down list.

Multiple Values  Select one or more values in a list.

Multiple Values  Select one or more values in a drop-down list.

Multiple Values  Search and select one or more values.

Single Value        Drag a horizontal slider to select a single value.

Wildcard Match Select values containing the specified characters.

What are the differences between Tableau desktop and Tableau Server?

Following are the difference between Tableau Desktop & Server:-

  • Tableau desktop is used to analyze and visualize the data, develop workbooks, dashboards, stories and visualization. While Tableau Server is an online tool for distribution, sharing and collaborating content develop in Tableau.
  • Tableau Desktop is installed on laptop or workstation while Tableau Server is installed on Windows server and accessed via browser.
  • Tableau Desktop develops the charts, reports, dashboard while Tableau Server is used to share those charts and reports.
  • Tableau Desktop offers analytic capabilities which cannot be found in Tableau Server. Tableau Desktop and Tableau Server has a rich development and authoring environment and offer brilliant security in development.
What are fact table and Dimension table in Tableau?

Facts are known as numeric metrics of the data which can be analyzed through dimension table. These facts are store in fact table containing foreing keys referring uniquely to dimension tables. Fact tables support data storage at the atomic level and allows number of records to be inserted at one time. For example, a Sales Fact table can have a product key, promotion key, customer key, items sold, referring to a specific event. Whereas dimensions are descriptive characteristic values for multiple dimensions of each attribute, defining multiple characteristics. A dimension table with a reference of a product key from the fact table, can consist of product name, product type, size, color, description, etc.

What is aggregation and disaggregation of data in Tableau?

Aggregation and disaggregation in Tableau are the ways to develop a scatter-plot to compare and measure the data values. Aggregation is the calculated form of set of values that return a single numeric value. When you place a measure on a shelf, Tableau automatically aggregates the data, usually by adding it. You can easily determine the aggregation applied to a field because the function always appears in front of the field’s name when it is placed on a shelf.

Disaggregating data refers to viewing each data source row, while analyzing data independently and dependently.  This allows you to view every row of the data source which can be useful when you are analysing measures that you may want to use both independently and dependently in the view.

Latest Blogs